Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts

Bellens, Simon; Probst, Gabriel; Janssens, Michel; Vandewalle, Patrick; Dewulf, Wim

doi:10.1016/j.polymertesting.2022.107540

Cited by 22 publications

(13 citation statements)

References 19 publications

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“…In industrial CT, UNet achieves good generalization and works under noisy conditions, e.g. for pore segmentation [18]. Recently, Mixed-Scale-Dense Networks showed better performance than UNet in cell segmentation tasks, and for medical CT data [19,20].…”

Section: Related Workmentioning

confidence: 99%

Three Step Volumetric Segmentation for Automated Shoe Fitting

Leipert¹,

Herl²,

Stebani³

et al. 2023

eJNDT

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This work presents a three-step segmentation process based on Convolutional Neural Networks. The task is to identify the different parts of shoes from Computed Tomography scans of boxed pairs of shoes. The first step of the three-step algorithm uses a scaled-down volume image to separate the shoe material from its surroundings. The second step segments the shoe's inside volume, i.e. the space enclosed by shoe material. The third and last step splits the segmented shoe material into individual components: shoe upper material, outer and insole. The complete process employs CNNs derived from three-dimensional UNets. Residual SE UNet, Dense UNet, and Bottleneck Residual UNet are evaluated for the three steps. The architectures are modified for large receptive fields. The networks are trained and tested for each step separately and conjointly on CT scans comprising various shoe types. The test results inspire hope for using the process for automated segmentation and extraction of meshes from large batches of CT scans. In particular, the first step using a Residual SE UNet achieves an F1-score of 88.2 % for shoes and 58.9 % for the packing material. The second step segments the inside volume with an F1-score of 81.0 %. The third step segments the shoe into its components and achieves an F1-score for insole of 79.5 %, outer sole of 88.7 % and upper material of 81.3 %.

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Section: Related Workmentioning

confidence: 99%

Three Step Volumetric Segmentation for Automated Shoe Fitting

Leipert¹,

Herl²,

Stebani³

et al. 2023

eJNDT

View full text Add to dashboard Cite

show abstract

“…The total acquisition time of an XCT scan is directly affected by the number of acquired x-ray projections and detector exposure time. For both, a decrease directly reduces the final reconstruction quality [10]. While faster XCT systems are becoming available on the market, a complementary decrease in acquisition time can still be achieved using more advanced algorithms to mitigate limited data and high noise levels.…”

Section: Introductionmentioning

confidence: 99%

Direct voxel classification from x-ray projections for 3D pore detection applied to laser sintered parts

Bellens,

Guerrero,

Janssens

et al. 2024

Meas. Sci. Technol.

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X-ray Computed Tomography (XCT) is a validated and frequently used tool to verify part geometry and to perform a non-destructive inspection of additive manufacturing parts. However, the acquisition of a large number of X-ray projections generates long inspection times. This conflicts with a high throughput of the production process and hinders the integration of XCT as an in-line quality control procedure for low-end parts. In this paper, we propose a method to obtain 3D information of the internal pores from a limited view or limited angle scan. The method combines a forward projection model of a cone-beam X-ray system and a deep learning neural network to directly classify each individual voxel, based on the X-ray projections in order to avoid the reconstruction and segmentation step. Accompanying reconstruction artefacts for limited view and limited angle XCT scans are thereby reduced, while preserving 3D information of the pores, defects or inclusions inside the material. The method is validated on real X-ray projections of polymer laser sintered industrial parts and shows a significant reduction in the required X-ray projections, hence acquisition time.

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“…While X-ray Computed Tomography (XCT) is a widely deployed measurement and inspection technique, the long acquisition times often hinder a cost-efficient integration. For conventional cone beam XCT, the acquisition of a large number of X-ray projections is time consuming and ranges from a few minutes up to hours [2]. The artefacts arising in limited view XCT scans drive the need for alternative reconstruction techniques, advanced analysis of the low-quality reconstructions or pre-processing of the acquired X-ray projections [3,4].…”

Section: Introductionmentioning

confidence: 99%

A machine learning supported sinogram interpolation method for X-ray computed tomography

Bellens¹,

Janssens²,

Vandewalle³

et al. 2023

eJNDT

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The time of an X-ray Computed Tomography (XCT) measurement is directly affected by the number of acquired X-ray projections. In general, a large number of X-ray projections, hence a large acquisition time is required to obtain a qualitative reconstruction and successive feature analysis. To expand the applicability of XCT towards low-end parts we propose a novel sinogram interpolation method that incorporates the object rotation. The method combines a forward projection model of the XCT system and a deep learning regression model to generate intermediate X-ray projections for XCT scans with a reduced number of projections. Thereby, the accompanying reconstruction artefacts can be reduced while preserving a lower acquisition time. The method is validated on simulated projections of the 3D Shepp-Logan phantom and simulated projections of real 3D printed components using the ASTRA-toolbox. Within the reconstructions, less streaking artefacts are observed and an increased contrast between different features is obtained without introducing rotational artefacts.

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Evaluating conventional and deep learning segmentation for fast X-ray CT porosity measurements of polymer laser sintered AM parts

Cited by 22 publications

References 19 publications

Three Step Volumetric Segmentation for Automated Shoe Fitting

Three Step Volumetric Segmentation for Automated Shoe Fitting

Direct voxel classification from x-ray projections for 3D pore detection applied to laser sintered parts

A machine learning supported sinogram interpolation method for X-ray computed tomography

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